A Survey of Fusion Genes in Myeloma Identifies Kinase Domain Activation Which Could be Targeted with Available Treatments
Notice bibliographique
Résumé
Abstract Introduction Although fusion genes other than the immunoglobulin (Ig) translocation, t(4;14), which results in IGH-WHSC1fusions, are not frequently detected in multiple myeloma (MM), recent evidence suggests that kinase fusion gene fusions do occur relatively frequently and may inform on treatment algorithms. Here we use a hybrid-capture based, next-generation sequencing assay to survey fusion genes in patients with MM. Methods We report on 1421 samples from 958 individuals diagnosed with either monoclonal gammopathy of unknown significance (MGUS), smoldering multiple myeloma (SMM) or MM who underwent targeted sequencing with the FoundationOne Heme® (F1H) assay. Tumor samples were obtained from bone marrow aspirates, enriched by CD138+selection using magnetic beads (AutoMACs, Miltenyi Biotech, Cologne, Germany or RoboSep, StemCell Technologies, Vancouver, Canada). RNA and DNA were extracted using the AllPrep DNA/RNA mini kit (Qiagen, Hilden, Germany), RNeasy RNA extraction kit (Qiagen) or Puregene DNA extraction kit (Qiagen). ≥ 50 ng of extracted DNA or RNA was processed on the F1H assay. The current assay analyzes the complete coding DNA sequence of 405 genes, as well as selected introns of 31 genes involved in chromosomal rearrangements as well as the RNA sequence of 265 commonly rearranged genes resulting in gene fusions. Genes included in this assay encode known or likely targets of therapy, either approved or in clinical trials, or are otherwise known drivers of oncogenesis. Sequencing was to an average depth of 510x and was performed using the Illumina HiSeq 2500. Sequences were analyzed for selected gene rearrangements including fusion genes which were detected by a combination of DNA and RNA sequencing. Results Rearrangements into the Ig loci were detected and included the 5 main translocations: t(4;14), t(6;14), t(11;14), t(14;16), and t(14;20), as well as translocations involving MYC at 8q24. From a combination of DNA capture and RNA-seq expression values we used 107 samples in a training set with matching gene expression profiling data to determine cut-offs for FGFR3, WHSC1, CCND3, MAF, MAFB, CCND2 and CCND1to stratify patients into the 5 main translocation groups. We used these values to classify a further 391 samples with corresponding gene expression profiling (GEP) data, resulting in sensitivities and specificities of t(4;14), 98% and 100%; t(6;14), 100% and 99%; t(11;14), 99% and 95%; t(14;16), 77% and 100%; t(14;20), 100% and 100%, respectively. 40 non-Ig rearrangements were detected in 38 patients (4.2%), of which 21 in-frame fusion genes were predicted. Recurrent fusion-genes, identified in more than one patient, included EIF4E3-FOXP1, TXNDC5-MYC and SUB1-WHSC1. As well as TXNDC5, MYC was also partnered with FOXO3, both of which are known partners of the MYC translocation. 12 of the 21 in-frame fusion genes involved kinase domains, including fusions with BRAF (n=4), NTRK3 (n=2), ALK (n=1), ROS1 (n=1), MAPK14 (n=1), MAP3K14 (n=1), FGFR1 (n=1), and DLG2 (n=1). Fusions involving each of these genes have been documented in other cancers. BRAF fusions are thought to partner with genes encoding homodimerization domains, resulting in downstream activation of Ras signaling. Other kinase fusions result in receptor signaling and downstream activation of the Ras signaling pathway. Of the patients with kinase fusions, 2 had an activating KRAS, NRAS or BRAF mutation but only one was clonal (84% cancer clonal fraction). One patient with samples taken at different timepoints had a GTF2I-BRAF fusion and concomitant KRAS G13C mutation (16% allele frequency), both of which were not detectable 8 months later but an AGK-BRAF fusion was detected at that time suggesting clonal selection. Conclusion Non-Ig fusion genes are present in myeloma patients, but at a low frequency. Most of the fusions detected contained a kinase domain indicating activation of the Ras signaling pathway, which is also activated through KRAS, NRAS and BRAF mutations in 50% of patients. Although rare (1%), these kinase fusions are potential clinical targets in myeloma where kinase inhibitors, such as crizotinib, can be used which has shown to be effective against ALK and ROS1 fusions. Disclosures He: Foundation Medicine, Inc: Employment, Equity Ownership. Zhong:foundation medicine: Employment. Bailey:Foundation Medicine, Inc: Employment, Equity Ownership. Vergillo:Foundation Medicine, Inc: Employment. Ross:Foundation Medicine, Inc: Employment. Miller:Foundation Medicine: Employment, Equity Ownership. Stephens:Foundation Medicine: Employment, Equity Ownership. Mughal:Foundation Medicine: Employment, Equity Ownership. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Janssen: Research Funding; Univ of AR for Medical Sciences: Employment; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».